{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "与'文化'相似度最高的10个词:\n",
      "[('地理', 0.6900621056556702), ('素养', 0.6724045276641846), ('现代', 0.6619790196418762), ('娱乐', 0.6514185070991516), ('艺术', 0.6466454863548279), ('社会科学', 0.645193338394165), ('交流', 0.640330970287323), ('哲学', 0.6400051712989807), ('交流史', 0.6372831463813782), ('民间', 0.6372312307357788)]\n"
     ]
    }
   ],
   "source": [
    "import numpy\n",
    "import gensim\n",
    "import numpy as np\n",
    "from jieba import analyse\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.models.word2vec import LineSentence\n",
    "def train_word2vec():\n",
    "    cor_data = open('TrainData.txt','r',encoding = 'utf-8')\n",
    "    model = Word2Vec(LineSentence(cor_data),sg = 0,vector_size = 200,window = 5,min_count = 5,workers = 9)\n",
    "    model.save('model_word2vec')\n",
    "    print(\"与'文化'相似度最高的10个词:\")\n",
    "    print(model.wv.most_similar('文化',topn = 10))\n",
    "train_word2vec()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def keyword(data):\n",
    "    tfidf = analyse.extract_tags\n",
    "    keywords = tfidf(data)\n",
    "    return keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_keywords(docpath, savepath):\n",
    "    with open(docpath, 'r', encoding = 'utf-8') as docf,open(savepath, 'w') as outf:\n",
    "        for data in docf:\n",
    "            data = data[:len(data)-1]\n",
    "            keywords = keyword(data)\n",
    "            for word in keywords:\n",
    "                outf.write(word + ' ')\n",
    "            outf.write('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pos(string,char):\n",
    "    space_pos = []\n",
    "    try:\n",
    "        space_pos = list(((pos)for pos, val in enumerate(string)if(val == char)))\n",
    "    except:\n",
    "        pass\n",
    "    return space_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_vector(file_name,model):\n",
    "    with open(file_name,'r') as f:\n",
    "        wordvec_size=200\n",
    "        word_vector=numpy.zeros(wordvec_size)\n",
    "        for data in f:\n",
    "            space_pos=get_pos(data,' ')\n",
    "            first_word=data[0:space_pos[0]]\n",
    "            if model.wv.__contains__(first_word):\n",
    "                word_vector=word_vector+model.wv[first_word]\n",
    "            for i in range(len(space_pos)-1):\n",
    "                word=data[space_pos[i]:space_pos[i+1]]\n",
    "                if model.wv.__contains__(word):\n",
    "                    word_vector=word_vector+model.wv[word]\n",
    "        return word_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "def similarity(vector1,vector2):\n",
    "    vector1_abs=np.sqrt(vector1.dot(vector1))\n",
    "    vector2_abs=np.sqrt(vector2.dot(vector2))\n",
    "    if vector2_abs !=0 and vector1_abs != 0:\n",
    "        similarity=(vector1.dot(vector2))/(vector1_abs * vector2_abs)\n",
    "    else:\n",
    "        similarity=0\n",
    "    return similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Dumping model to file cache C:\\Users\\ADMINI~1\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.922 seconds.\n",
      "Prefix dict has been built successfully.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "文本new1的部分向量:\n",
      " [ 0.26054685 -0.08745985 -0.36443845  1.17007749  0.60770731 -0.23784898\n",
      "  0.4443037   0.86172754 -0.86832286 -0.3796947  -0.54991934  0.26778319\n",
      "  0.29822842  0.84132905 -0.26605362 -0.700908    0.09000368  0.40982602\n",
      "  0.87575959 -1.38365021]\n",
      "文本new2的部分向量:\n",
      " [ 0.82088266 -0.25889248 -0.80111067  1.60448352  1.24345001 -0.01138981\n",
      "  0.12554734  1.28954788 -1.85423075 -0.67184932 -1.23284835  0.71551119\n",
      "  0.26207149  1.76964322 -1.20055355 -1.27956249  0.35934003  0.78237658\n",
      "  0.46894306 -1.61645029]\n",
      "文本new1和new2的相似度: 0.7059144102493587\n"
     ]
    }
   ],
   "source": [
    "def main():\n",
    "    model=gensim.models.Word2Vec.load('model_word2vec')\n",
    "    new1='new1.txt'\n",
    "    new2=\"new2.txt\"\n",
    "    new1_keywords='new1_keywords.txt'\n",
    "    new2_keywords='new2_keywords.txt'\n",
    "    get_keywords(new1,new1_keywords)\n",
    "    get_keywords(new2,new2_keywords)\n",
    "    new1_vector=get_vector(new1_keywords,model)\n",
    "    print('文本new1的部分向量:\\n',new1_vector[:20])\n",
    "    new2_vector=get_vector(new2_keywords,model)\n",
    "    print('文本new2的部分向量:\\n',new2_vector[:20])\n",
    "    print(\"文本new1和new2的相似度:\",similarity(new1_vector,new2_vector))\n",
    "if __name__=='__main__':\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
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   "source": []
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